A neural signature for the subjective experience of threat anticipation under uncertainty

Uncertainty about potential future threats and the associated anxious anticipation represents a key feature of anxiety. However, the neural systems that underlie the subjective experience of threat anticipation under uncertainty remain unclear. Combining an uncertainty-variation threat anticipation paradigm that allows precise modulation of the level of momentary anxious arousal during functional magnetic resonance imaging (fMRI) with multivariate predictive modeling, we train a brain model that accurately predicts subjective anxious arousal intensity during anticipation and test it across 9 samples (total n = 572, both gender). Using publicly available datasets, we demonstrate that the whole-brain signature specifically predicts anxious anticipation and is not sensitive in predicting pain, general anticipation or unspecific emotional and autonomic arousal. The signature is also functionally and spatially distinguishable from representations of subjective fear or negative affect. We develop a sensitive, generalizable, and specific neuroimaging marker for the subjective experience of uncertain threat anticipation that can facilitate model development.


Control experiment: number rating anticipation
To rule out the possibility that the developed model SUITAS was confounded by the color of the cue or the motor-related response, we designed and conducted an fMRI experiment aimed at controlling for the effect of color or preparatory motor response.
Participants were shown five different colored numbers from 1-5, each with a specific color (Supplementary Fig. 3).They were required to anticipate the number that they were going to rate during the number presentation stage and rate according to what they previously saw on a scale ranging from 1 to 5 at the end of each trial.The duration for each phase was kept the same as the UVTA paradigm.Stimuli were presented using the E-Prime software (Psychology Software Tools, Sharpsburg, PA).
We collected an independent dataset (n = 20, 8 females, 18-26 years, 21.30 ± 2.74) to test potential confounding effects of the processes on the SUITAS.Imaging data acquisition and preprocessing were identical to Studies 1-3.The first-level GLM model included five separate boxcar regressors time-logged to the anticipatory cue period corresponding to each rating, which allowed us to model brain activity in response to each trial (i.e., preparatory motor response) separately.The SUITAS was applied to the first-level activation map for each number rating to obtain the signature responses, which were next correlated with the true ratings across participants.

Study 4: visual threat-conditioning dataset
The visual threat-conditioning dataset was from a previous pharmacological study that examined the effect of angiotensin II type 1 receptor antagonist losartan (LT) in threat extinction learning 1 .The brain activations during threat acquisition phase were used to test the generalizability of our predictive model (i.e., SUITAS) in predicting uncertain threat anticipation during associative learning across cohorts, paradigms, MRI systems and scanning parameters.
In this study, 59 male participants (losartan, group: n = 30, mean ± SD age = 20.50 ± 1.80; Placebo group: n = 29, mean ± SD age = 20.86 ± 1.68) underwent an visual threat-conditioning paradigm during fMRI scanning.Importantly the active treatment (losartan) was only administered following the conditioning procedure, avoiding confounding treatment effects in the context of the current study.Two colored squares (red and blue) were used as conditioned stimuli (CSs).One colored square (red) was designated as the CS+ and was pseudorandomly paired with a mild electric shock (unconditioned stimulus, US, 2 ms) on 43% of the trials, whereas the other colored square (blue) was never paired with shock (CS-).All CSs were presented for 4 s, with a 9-12s interstimulus interval (ISI).The acquisition included two runs and each run consisted of 8 unreinforced CS+, 8 CS-, intermixed with 6 CS+ paired with the shock.
The participants were instructed to pay attention to the stimuli and find out the relationship between the stimuli and the shocks.The shock was generated by a Biopac stimulator module STM100C and a STIMSOC adapter (Biopac Systems, Inc.) and the intensity level was determined by a work-up procedure (details and the extinction phase please see ref. 1 ).MRI data were collected on a 3.0-T Siemens TRIO system with a 12-channel head coil (Siemens, Erlangen, Germany).Imaging data were preprocessed and analyzed using SPM12 (scanning parameters, preprocessing and analysis steps see ref. 1 ).

Study 5: auditory threat-conditioning dataset
The auditory threat-conditioning dataset was from a previous study that developed a threat-predictive pattern 2 .This dataset served the same purpose as Study 4 with a different modality of conditioned stimuli (CSs).
In this study, 68 participants (45 females, mean ± SD age = 29.64 ± 15.89) underwent an auditory threat-conditioning paradigm during fMRI acquisition.Two different tones (800 and 170 Hz) were used as CSs.One of the tones was designated as the CS+ and was paired with a mild electric shock (unconditioned stimulus, US) on 33% of the trials, whereas the other tone was never paired with shock (CS-).All CSs were presented for 4 s, with a 10 s fixed inter-trial-interval (ITI).Acquisition included 8 randomized repetitions of CS+ paired with shock, 8 randomized repetitions of unreinforced CS+, and 8 randomized repetitions of CS-.The participants were told that they might receive shocks during the experiment without knowing the contingency.The tones were delivered through MR-designed headphones, and were outside of the range of scanner noise.Mild electric shocks were delivered through a bar electrode attached to the left wrist.The shock intensity was determined by a work-up procedure (details and the extinction phase please see ref. 2 ).MRI data were collected on a 3.0-T Siemens Allegra scanner and a 32 channel Siemens head coil.Imaging data were preprocessed and analyzed using SPM8 and custom Matlab (MATLAB, The MathWorks, Inc., Natick, MA) code (scanning parameters, preprocessing and analysis steps see ref. 2 ).

Study 6: thermal pain dataset
In the corresponding study by Wager et al. 3 (see also Woo et al. 4 ), 33 healthy participants (22 females; mean ± SD age = 27.9 ± 9.0 years) underwent a thermal pain paradigm in which six distinct temperatures (44.3-49.3℃ in 1℃ increments) were delivered to the left forearm during fMRI acquisition using a TSA-II Neurosensory Analyzer (Medoc Ltd.) with a 16-mm Peltier thermode end-plate.Each thermal stimulation lasted 12.5 s (3 s ramp-up, 2 s ramp-down, and 7.5 s target temperature).After a 4.5-8.5 s jitter, participants judged whether the stimulus was painful or nonpainful (4 s) and provided a pain rating on a 100-point visual analogue scale from "no pain" to "worst imaginable pain" (7 s).There are 9 functional runs in total.Runs 1, 2, 4, 8 and 9 include 11 stimulations for each temperature level 1-5.In runs 5-6, temperatures were increased one degree, with 4 stimuli at each of levels 2-6.During run 3 and 7, participants were asked to cognitively regulate the pain intensity in which ten randomly stimulations were presented.The "regulation'' runs (run 3 and 7) were not included in generating the thermal pain dataset.Stimulus presentation and behavioral data acquisition were controlled using E-Prime software (PST Inc.).
Whole-brain fMRI data were acquired on a 3T Philips Achieva TX scanner (imaging data acquisition parameters see Woo et al., 2015 4 ).Structural and functional MRI data were preprocessed and analyzed using SPM8 (scanning parameters, preprocessing and analysis steps see Woo et al., 2015 4 ).

Study 7: monetary gain/loss anticipation dataset
The dataset in Study 7 was taken from an ongoing study from College Student Cohort of Zhangjiang International Brain Biobank (https://zib.fudan.edu.cn) which used the monetary incentive delay (MID) task, one of the classical and widely used fMRI paradigms for reward processing 5 , to assess reward/punishment processing with gain/loss conditions for small or large amounts of money (i.e., -5.0 ¥, -0.2 ¥, 0, 0.2 ¥ or 5.0 ¥) and a neutral (no gain or loss) condition 6 .The present MID task is identical to the MID paradigm from the Adolescent Brain Cognitive Development (ABCD) study 7 , and was implemented in healthy young adults in China.In each trial, participants first saw one of three cue shapes (circle, square, or triangle) representing reward, punishment, or no reward/punishment condition, respectively, with the corresponding word inside the cue shape and the amount of money below the cue shape.The cue appears for 2 seconds followed by a 1.5-4 second delay phase (black cross).Participants anticipated reward or punishment during the cue presentation and delay phases.Then, a blank target cue (same shape as the cue presented earlier) appeared on the screen, and participants had to quickly press the response button to either receive a reward or avoid a punishment.After a short delay (1.5-1.85 s), the feedback for the current trial (i.e., the amount of monetary gain or loss) and the accumulated reward will appear.The entire fMRI experiment consisted of 2 runs, each with 50 trials (10 trials per experimental condition), and lasting approximately 5.5 minutes.The study conforms to Fudan University Institutional Review Board's rules and procedures, and all participants provide informed consent.We randomly selected 100 participants (18.68 ± 0.87 years old, 63 females) to control for potential explanation of general anticipation.Stimulus were presented using E-prime Psychology Software (PST Inc.).
Magnetic resonance imaging (MRI) data were collected on a 3T Siemens Prisma.Skin conductance data were downsampled to 100 Hz, filtered using a 1 Hz lowpass filter, and square root-transformed to normalize the distribution using Biopac Acqknowledge 4.2.0 software.Using custom MATLAB code, we extracted the maximum values of the skin conductance levels (SCLs) in a time window of 1-8 s following the cue/anticipation onset and removed the baseline SCL value (the mean) in a 1 s window before the cue onset from the maximum for each trial of each participant, in line with previous methodologies 9 .For the subsequent model prediction, the trials were grouped into quintiles based on the SCL values for each participant and the brain activation maps were binned (i.e., averaged) from 1 to 5 (anticipation period, one map per level) to reflect different SCLs.

Study 10: fear dataset
The fear dataset was from our previous study that developed a subjective fear decoder 10 .The fear induction paradigm used fearful pictures selected from the IAPS (International Affective Picture System), NAPS (Nencki Affective Picture System) and additional pictures from the internet.The study included n = 67 adults (discovery cohort: 34 females, mean ± SD age = 21.5 ± 2.1 years).80 photographs were distributed in 4 fMRI runs with each presented once.Each trial consisted of a 6-s picture presentation period followed by a 2 s fixation-cross separating the stimuli from the rating period (4 s).Participants reported the fearful state they experienced during the picture presentation using a 5-point Likert scale from 1 (neutral/very slight fear) to 5 (very high fear).Stimuli were presented using E-Prime software (Version 2.0; Psychology Software Tools, Sharpsburg, PA).Another validation cohort in this study includes 20 participants (6 females; mean ± SD age = 21.75 ± 2.61 years) who underwent a similar task in the fMRI scanner as the discovery cohort and also provided ratings of fear experience (details see ref. 10 ).The generalization cohort for the fear model include 31 participants (15 females; mean ± SD age = 23.29 ± 4.21 years) who underwent a fMRI session where they were presented with 3600 fearful images consisting of 30 animal categories and 10 object categories (details see ref. 9,10 ).MRI data were collected on a 3.0-T GE Discovery MR750 system (General Electric Medical System, Milwaukee, WI, USA).Structural and functional MRI data were preprocessed and analyzed using SPM12 (scanning parameters, preprocessing and analysis steps see ref. 10 ).

Study 11: negative affect dataset
The negative affect dataset was from a previous study developing a multivariate pattern that predicts subjective ratings of negative emotion 11 .A total of 183 participants (female = 52%, mean ± SD age = 42.77± 7.3 years) were recruited and divided into a training set (n = 121) and a hold-out test dataset (n = 61).Stimuli consisted of 15 negative photographs and 15 neutral photographs selected from the IAPS.Each trial begins with a fixation cross (~2 s) followed by a text instruction cue ('Look', 2 s).
Participants then see a 7-s presentation of negative or neutral images and are asked to rate their emotional state on a Likert scale from 1 (neutral) to 5 (strongly negative).
Finally, there was a jittered rest period (1-3 s).Stimuli were presented using the E-Prime software (Psychology Software Tools, Sharpsburg, PA).
Imaging data were acquired on a 3T Trio TIM whole-body scanner (Siemens, Erlangen, Germany) using a 12-channel, phased-array head coil.fMRI data were preprocessed and analyzed using SPM8 and custom MATLAB (MATLAB, The MathWorks, Inc., Natick, MA) code (scanning parameters, preprocessing and analysis steps see ref. 11 ).

Supplementary Results
Validation of the candidate mechanism of the paradigm -associations between trait anxiety and intolerance to uncertainty and subjective anxious arousal under uncertainty during the UVTA We ran two separate linear mixed-effects models (LMMs) with trait anxiety (TA) or intolerance of uncertainty (IOU) and uncertainty condition (with safe as baseline: high vs. safe, medium vs. safe, low vs. safe) as fixed effects using the lme4 package in R (version 4.3.1).With all participants across Study 1-3 (n = 124), we found that there was no significant main effect of TA (F(1,122) = 0.77, P = 0.38), nor interaction between TA score and uncertainty condition (F(2,244) = 0.34, P = 0.71) on subjective ratings, which may reflect a low specificity of the STAI for measuring anxiety 12 .A main effect of uncertainty condition was observed (F(2,244) = 7.76, P < 0.001, η 2 p = 0.06 [0.02, 1.00]).

SUITAS prediction of the preparatory motor response
To investigate whether our SUITAS model was also sensitive to predict motor response anticipation corresponding to different colors, we applied the SUITAS to the activation maps modeled for each rating of the control experiment in an independent dataset (n = 20).Results showed that the SUITAS could not predict the preparatory/anticipatory motor activity based on the colored cue (r = 0.072, p = 0.476, see Supplementary Fig. 4).Therefore, we ruled out the possibility that the neural patterns of the SUITAS coded motor response.However, the trial-wise SCLs and subjective anxious experience did not correlate with each other at the individual level (mean r = 0.12, p = 0.32, bootstrap test, one-sided Supplementary Fig. 7b), suggesting that the objectively measured physiological responses and self-reported subjective experience is dissociated in representing momentary anxious arousal (see recent discussions in ref. 9,13 ).To further validate that the neural basis of subjective anxious arousal is dissociated from that of physiological arousal, the SUITAS was applied to the binned beta images of anticipation stage corresponding to different SCLs and results showed that the SUITAS predicted SCLs (r = 0.216, p < 0.001) with a much smaller effect size than predicting subjective ratings (r = 0.556, p < 0.001, difference in effect size: ∆r = 0.34, one-tailed permutation test P < 0.001, Supplementary Fig. 7c), demonstrating that the SUITAS was specific to predict subjective experience of anxious arousal during uncertain threat anticipation.

Spatial correlations among thresholded maps of SUITAS, VIFS and PINES
The Pearson correlations decreased after thresholding these signatures at 3-dimensinal T1-weighted images (0.8 mm isotropic, TR = 2500 ms, TE = 2.25 ms) were acquired with a gradient-echo sequence for anatomical localization and coregistration.High spatial (2.0 mm isotropic) and temporal (TR = 800 ms) resolution functional MRI time series were acquired with echo-planar imaging sequence.All functional images were preprocessed with recommended protocols from fMRIPrep (version 20.2.3, https://fmriprep.org) 8and included the following major steps: (i) T1wreference workflow; (ii) correction for susceptibility distortions based on fieldmap; (iii) head-motion correction; (iv) co-registration to T1w-reference and normalization to MNI standard space; (v) spatial smoothing with a 6 mm full-width at half-maximum (FWHM) Gaussian kernel and temporal detrending.The first-level GLM model included the combinations of the following regressors of interest Target (Hit or Miss) * Phases (Anticipation, Feedback) * Task Conditions (large-loss, small-loss, neutral, small-win or large-win) and 26 additional covariate regressors (i.e., 24 motion-related parameters: 6 rigid-body motion parameters, their first temporal derivatives and 12 corresponding squared items; as well as mean signals of both white matter and ventricles).In line with the aim of the present study, the contrasts of interest included positive anticipation (mean of small-and large-win anticipation versus implicit baseline) and negative anticipation (mean of small-and large-loss anticipation versus implicit baseline) which were classified by the SUITAS relative to neutral anticipation (no win or loss).Study 8: visually-induced emotional arousal dataset Study 6 served to control for arousal and presented arousing emotional and nonarousing neutral pictures during fMRI.The participants (n = 48, 25 females, mean  SD age = 20.10 ± 2.20 years) of Study 8 were from Study 3 to study emotional processing in healthy adults, in which data from 2 participants were missing.Thirtytwo emotionally arousing pictures (16 negative, i.e., disgust, 16 positive, i.e., joy, adoration, amusement) and 16 neutral were selected from the IAPS (International Affective Picture System) and NAPS (Nencki Affective Picture System) and determined by arousal ratings (mean in the 1-7 arousal scale: high-arousing negative, 5.17; neutral, 2.02; high-arousing negative versus neutral: t21 = 10.73,P < 0.001) from an independent sample (n = 22, 8 females, mean  SD age = 18.95 ± 0.82 years).The participants were asked to passively view the pictures.Stimuli were presented using the E-Prime software (Version 2.0; Psychology Software Tools, Sharpsburg, PA).Stimuli were presented in a single fMRI run (4 min 48 s in total, 2 s picture presentation and 3-5 s fixation per trial) in a pseudorandom order with no more than two consecutive trials of the same category.Imaging data acquisition and preprocessing were identical to Study 3. The first-level GLM model included one regressor of interest for each experimental conditionthe picture viewing period.The SUITAS was tested by means of classifying highly arousing disgust picture viewing period versus implicit baseline (fixation) compared to neutral picture viewing period versus implicit baseline.Study 9: physiological arousal dataset We collected skin conductance data during fMRI experiments via an MRIcompatible Biopac system (MP-150) in Studies 1-3.Skin conductance (1000 Hz) was sampled using MRI-compatible disposable, radiotranslucent, pre-gelled electrodes (EL508) attached to the index and middle fingers of the non-dominant (left) hand.